The Undefined Success Problem
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The Undefined Success Problem
Most AI projects fail before they get started — not because of technology, but because nobody defined what success looks like.
A course creator buys an AI writing tool because everyone says it’ll speed up content production. Six months and several hundred dollars later, someone asks: “Did it work?”
The honest answer: “I don’t know, because I never defined what ‘working’ meant.”
Projects without clear metrics can’t fail. There’s no definition of failure. So they drift indefinitely, consuming budget while delivering “lessons learned” instead of results.
Veljko Krunic puts it directly in Succeeding with AI: “If you can’t quantify the business result you’re hoping to achieve, you have to ask yourself and your stakeholders whether the project is worth doing.”
The Metric Mandate: Before any AI project moves past the idea stage, it must answer five questions — what number will change, what that number is today, what improvement justifies the cost, when you’ll measure, and what result means you stop. Chapter 2 covers these in detail. For now: if you can’t answer all five, the project is still a wish, not a plan.
Without clear metrics, you end up keeping tools out of inertia rather than conviction — unable to tell what’s helping from what’s just sitting there.